山西大学学报（自然科学版）2020，Vol.43Issue(4)：746-755,10.DOI:10.13451/j.sxu.ns.2020061
一个新的针对新颖性和多样性推荐的矩阵分解模型
A New Matrix Factorization Model for Novel and Diverse Recommendation
摘要
Abstract
Modern recommendation algorithms focus on novel items and diverse recommendation list for at-tracting users.Because a collaborative filtering based recommendation algorithm usually generates similar items for accuracy,it is a challenge to find novel and diverse items while keeping accuracy.Most of the ex-isting studies developed two-step recommendation models that optimize accuracy first and then diversity,and the two-step optimized model generated diverse items at the sacrifices of accuracy due to the conflict of the optimization goals(diversity and accuracy).We propose a new matrix factorization model,that simul-taneously optimizes novelty,diversity and accuracy.The new constraint that makes the latent vector of the target user close to the average latent factors of the users who have rated long-tail items was developed for novel recommendations.And the other new constraint that makes each item latent close to the mean of all i-tem latent,was designed for diversity recommendation lists.The comprehensive experiments were conduc-ted on the Movielens100K,Epinions and Rym dataset.Experimental results demonstrated the superior performance in terms of accuracy,aggregate diversity,individual diversity and novelty to the state of the art models.关键词
推荐算法/新颖性/多样性/矩阵分解Key words
recommendation algorithm/novelty/diversity/matrix factorization分类
信息技术与安全科学引用本文复制引用
赵鹏,彭甫镕,崔志华,荆雪纯,任珂舟..一个新的针对新颖性和多样性推荐的矩阵分解模型[J].山西大学学报（自然科学版）,2020,43(4):746-755,10.基金项目
国家自然科学基金(61802238 （）
61672332) （）
山西省重点研发计划(国际科技合作)项目(201903D421003 （国际科技合作）
201903D121119 （）
201903D321039) （）
山西省高等学校科技创新项目(201802013) （201802013）
山西省青年基金项目(201901D211168) （201901D211168）
